Learning Stochastic Binary Tasks using Bayesian Optimization with Shared Task Knowledge

Robotic systems often have tunable parameters which can affect performance; Bayesian optimization methods provide for efficient parameter optimization, reducing required tests on the robot. This paper addresses Bayesian optimization in the setting where performance is only observed through a stochastic binary outcome – success or failure. We de- fine the stochastic binary optimization problem, present a Bayesian framework using Gaussian processes for classification, adapt the existing expected improvement metric for the binary case, and benchmark its performance. We also exploit problem structure and task similarity to generate principled task priors allowing efficient search for diffi- cult tasks. This method is used to create an adaptive policy for climbing over obstacles of varying heights.